Time series forecasting with exogenous variables: a literature review to identify promising gaps in computational research

Abstract

This study presents a comprehensive literature review on integrating exogenous variables in time series forecasting, with a particular focus on financial markets. Both traditional statistical methods, such as ARIMA and SARIMA, and machine learning techniques, like neural networks and support vector regression, are systematically examined, emphasizing their impact on prediction accuracy and the practical challenges involved. Significant research gaps are identified through an in-depth analysis of the existing literature, especially regarding the application of hybrid models and advanced computational techniques. Unlike previous reviews, this paper highlights the potential of combining traditional and machine learning approaches to handle multidimensional data from various external sources better. Addressing these gaps could enhance the robustness and precision of financial forecasting models, offering valuable insights to academic researchers and industry practitioners navigating volatile market environments. The paper consolidates current knowledge and underscores promising computational opportunities that could transform time series forecasting.

Keywords

• computational techniques
• exogenous variables
• financial markets
• time series forecasting